Using piper trilinear diagrams and principal component analysis to determine variation in hydrochemical faces and understand the evolution of groundwater in sidi slimane region, Morocco

Abstract

The water quality of groundwater is one of the most important issues for development and life societies. The chemical parameters of the groundwater play a significant role in classifying and assessing water quality. In this study, twenty samples were collected from different locations in a rural region to determine the hydrochemical facies characteristics and understand the evolution of groundwater quality in the Sidi Slimane area in Morocco. Piper trilinear diagram and principal component analysis (PCA) were used to find out the major contributing parameters involved in groundwater samples. The results showed that the analytical values obtained when plotted on Piper’s plots revealed that the alkalis (Sodium, Potassium), are significantly dominant over alkaline earth metals (Calcium, Magnesium) and the strong acidic anions (Chlorides, Sulfates) dominant over the weak acidic anions (Carbonates and Bicarbonates). Piper trilinear diagram classified 90 % of the samples from the study area under Na+-K+-Cl--SO2-4 type and only 10 % samples under Ca2+-Mg2+-Cl--SO2-4 type. the Piper plots highlighted that the groundwater is dominant of Na–Cl type and the water types originating from halite dissolution (saline) in the study area. Also, correlation matrix showed that 95%, 100%, 65%, 75%, 65% and 55% of the examined groundwaters exhibit higher concentration of Sodium, Ammonium, Calcium, Electrical Conductivity EC, Chloride, and Nitrate respectively than the maximum acceptable limit of the Moroccan’s standards for drinking water. PCA suggests a significant mutual positive correlation between two main groups of parameters (EC, Ca2+, Mg2+, K+, Na+-, HCO3 and Cl-) and (pH, NH+4, SO2-4, and NO-3). The output of PCA analysis is in confirmation with that of the Piper trilinear diagram and the correlation matrix to identify the characteristics of the samples and aid to comprehend their spatial distribution.